Mar 2, 2019 · We opt for top-down recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on ...

This is achieved by learning to predict the node type which determines how to decompose a node and when to stop the decomposition. • Node segmentation module ...

We opt for topdown recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural ...

Starting from a full shape represented as a point cloud, our model performs recursive binary decomposition, where the decomposition network at all nodes in the ...

PartNet: A Recursive Part Decomposition Network for Fine-grained and. Hierarchical Shape Segmentation – Supplemental Material ... PartNet segmentation. This way ...

Another parallel line of work focuses on learning the concept of universal object parts and decomposing a 3D shape into a set of (hierarchical) fine-grained ...

We opt for topdown recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural ...

PartNet: A Recursive Part Decomposition Network for Fine-Grained and Hierarchical Shape Segmentation · Figures and Tables · Topics · Ask This Paper · 85 Citations ...

The symmetry hierarchies were used to train the PartNet model proposed in our paper "PartNet: A Recursive Part Decomposition Network for Fine-grained and ...

In their dataset, each object is usually decomposed into 2∼5 coarse semantic parts. PartNet provides more fine-grained part annotations that contains 18 parts ...